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mehak-sachdeva
2017-02-01 19:16:32 -05:00
parent d7bccc1063
commit 8c5449cfd0
3 changed files with 62 additions and 26 deletions
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src/pg/sql/05_segmentation.sql
+5 -4
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@@ -48,16 +48,17 @@ CREATE OR REPLACE FUNCTION
min_samples_leaf INTEGER DEFAULT 1)
RETURNS TABLE (cartodb_id TEXT, prediction NUMERIC, accuracy NUMERIC)
AS $$
from crankshaft.segmentation import create_and_predict_segment
from crankshaft.segmentation import Segmentation
seg = Segmentation()
model_params = {'n_estimators': n_estimators, 'max_depth':max_depth, 'subsample' : subsample, 'learning_rate': learning_rate, 'min_samples_leaf' : min_samples_leaf}
return create_and_predict_segment(query,variable_name,target_table, model_params)
return seg.create_and_predict_segment(query,variable_name,target_table, model_params)
$$ LANGUAGE plpythonu;
CREATE OR REPLACE FUNCTION
CDB_CreateAndPredictSegment (
query TEXT,
variable_name TEXT,
target_table TEXT,
target_query TEXT,
feature_columns TEXT[],
n_estimators INTEGER DEFAULT 1200,
max_depth INTEGER DEFAULT 3,
@@ -69,5 +70,5 @@ AS $$
from crankshaft.segmentation import Segmentation
seg = Segmentation()
model_params = {'n_estimators': n_estimators, 'max_depth':max_depth, 'subsample' : subsample, 'learning_rate': learning_rate, 'min_samples_leaf' : min_samples_leaf}
return seg.create_and_predict_segment(query,variable_name,target_table, model_params)
return seg.create_and_predict_segment(query, variable_name, feature_columns, target_query, model_params)
$$ LANGUAGE plpythonu;